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Visualisation with treemaps and sunbursts in many-objective optimisation.

David J Walker1

  • 1University of Exeter, Exeter, UK.

Genetic Programming and Evolvable Machines
|April 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces treemaps and sunbursts for visualizing complex data in many-objective evolutionary algorithms. These methods effectively display dominance relations, diversity, and convergence in search populations.

Keywords:
Evolutionary computationMany-objective optimisationVisualisation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Visualisation is crucial for understanding evolutionary computation algorithms.
  • Presenting complex data from many-objective evolutionary algorithms (MaOEAs) is challenging.
  • Existing visualization methods may not adequately represent dominance relations in MaOEAs.

Purpose of the Study:

  • To explore the use of treemaps and sunbursts for visualizing data in many-objective evolutionary algorithms.
  • To develop a novel algorithm for arranging treemaps to display dominance relations.
  • To demonstrate the effectiveness of these visualization techniques for evolutionary computation.

Main Methods:

  • Developed a novel treemap arrangement algorithm to explicitly show dominance relations.
  • Investigated approaches for creating tree structures to represent multi- and many-objective solutions.
  • Applied treemaps and sunbursts to visualize diversity and convergence in search populations.

Main Results:

  • Treemaps and sunbursts can effectively visualize key aspects of evolutionary computation.
  • The novel treemap algorithm successfully displays dominance relations in many-objective populations.
  • Demonstrated the utility of these methods on various test problems and a real-world case.

Conclusions:

  • Treemaps and sunbursts offer a powerful approach for visualizing complex data in many-objective evolutionary algorithms.
  • These techniques enhance the understanding of algorithm operation and result presentation.
  • The proposed methods provide valuable insights into population diversity and convergence.